tegwick
162a2ae93c
Added two new Claude Code subagents following proper specification format: **Kaizen Optimizer Agent:** - Meta-agent for analyzing and optimizing other subagents - Performance analysis and specification improvement recommendations - Agent ecosystem health assessment and continuous improvement - Proper YAML frontmatter with proactive usage guidelines **Refactoring Assistant Agent (Optimized):** - Streamlined from 19-section complex specification to focused Claude Code format - Code quality assessment and refactoring guidance within Claude Code environment - Security analysis and performance optimization recommendations - Integration with existing agent ecosystem (tddai-assistant, general-purpose, project-assistant) **Also includes Issue #15 AST Query CLI implementation:** - AST Service with display, query, and statistics capabilities - JSONPath integration for flexible AST navigation - CLI commands: ast-show, ast-query, ast-stats (22/22 tests passing) - Leverages existing cache system for optimal performance 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
270 lines
8.9 KiB
Python
270 lines
8.9 KiB
Python
"""
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AST Service for Issue #15 - AST Query and Analysis functionality.
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This service provides high-level AST operations for the CLI commands:
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- AST display and visualization
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- JSONPath querying of AST structures
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- Statistical analysis of document content
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Leverages the existing AST cache system for optimal performance.
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"""
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import json
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import sys
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from collections import Counter
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from pathlib import Path
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from typing import Dict, List, Any, Optional
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from jsonpath_ng import parse as jsonpath_parse
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from .ast_cache import ASTCache
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from .cache_service import CacheDirectoryService
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class ASTService:
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"""
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Service for AST introspection and analysis operations.
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Provides high-level operations for CLI commands while leveraging
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the existing AST cache system for performance optimization.
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"""
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def __init__(self):
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"""Initialize AST service with cache integration."""
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self.cache_service = CacheDirectoryService()
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cache_dir = self.cache_service.get_cache_directory()
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self.ast_cache = ASTCache(cache_dir)
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def display_ast(self, file_path: Path, format_type: str = "tree") -> Dict[str, Any]:
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"""
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Display AST structure for a markdown file.
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Args:
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file_path: Path to markdown file
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format_type: Display format (tree, json, compact)
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Returns:
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Dictionary with display results and metadata
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"""
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try:
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if not file_path.exists():
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return {
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'success': False,
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'message': f'File not found: {file_path}',
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'output': ''
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}
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# Load AST using cache system
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ast = self.ast_cache.load_cached_ast(file_path)
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if format_type == "json":
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output = json.dumps(ast, indent=2, ensure_ascii=False)
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elif format_type == "compact":
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output = self._format_ast_compact(ast)
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else: # tree format (default)
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output = self._format_ast_tree(ast)
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return {
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'success': True,
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'message': f'AST structure for {file_path.name}',
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'output': output,
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'token_count': len(ast)
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}
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except Exception as e:
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return {
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'success': False,
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'message': f'Error displaying AST: {e}',
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'output': ''
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}
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def query_ast(self, file_path: Path, jsonpath_expr: str) -> Dict[str, Any]:
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"""
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Query AST using JSONPath expressions.
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Args:
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file_path: Path to markdown file
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jsonpath_expr: JSONPath query expression
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Returns:
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Dictionary with query results and metadata
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"""
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try:
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if not file_path.exists():
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return {
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'success': False,
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'message': f'File not found: {file_path}',
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'matches': [],
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'count': 0
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}
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# Load AST using cache system
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ast = self.ast_cache.load_cached_ast(file_path)
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# Parse JSONPath expression
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try:
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jsonpath_expr_parsed = jsonpath_parse(jsonpath_expr)
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except Exception as e:
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return {
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'success': False,
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'message': f'Invalid JSONPath syntax: {e}',
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'matches': [],
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'count': 0
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}
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# Execute query
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matches = jsonpath_expr_parsed.find(ast)
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results = [match.value for match in matches]
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return {
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'success': True,
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'message': f'JSONPath query results for {file_path.name}',
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'matches': results,
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'count': len(results),
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'query': jsonpath_expr
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}
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except Exception as e:
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return {
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'success': False,
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'message': f'Error executing query: {e}',
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'matches': [],
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'count': 0
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}
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def analyze_ast_statistics(self, file_path: Path) -> Dict[str, Any]:
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"""
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Generate comprehensive statistics about AST structure.
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Args:
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file_path: Path to markdown file
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Returns:
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Dictionary with detailed statistics
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"""
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try:
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if not file_path.exists():
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return {
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'success': False,
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'message': f'File not found: {file_path}',
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'statistics': {}
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}
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# Load AST using cache system
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ast = self.ast_cache.load_cached_ast(file_path)
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stats = self._calculate_ast_statistics(ast)
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return {
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'success': True,
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'message': f'AST statistics for {file_path.name}',
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'statistics': stats
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}
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except Exception as e:
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return {
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'success': False,
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'message': f'Error analyzing statistics: {e}',
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'statistics': {}
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}
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def _format_ast_tree(self, ast: List[Dict[str, Any]]) -> str:
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"""Format AST as a tree structure."""
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lines = []
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for i, token in enumerate(ast):
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level = token.get('level', 0)
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indent = ' ' * level
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token_type = token.get('type', 'unknown')
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# Add some content info for readability
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content_info = ""
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if token.get('content'):
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content_preview = token['content'][:30]
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if len(token['content']) > 30:
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content_preview += "..."
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content_info = f' "{content_preview}"'
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elif token.get('tag'):
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content_info = f' <{token["tag"]}>'
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lines.append(f'{indent}[{i:2d}] {token_type}{content_info}')
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return '\n'.join(lines)
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def _format_ast_compact(self, ast: List[Dict[str, Any]]) -> str:
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"""Format AST in compact form."""
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lines = []
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for token in ast:
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token_type = token.get('type', 'unknown')
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if token.get('content'):
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content = token['content'][:20]
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if len(token['content']) > 20:
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content += "..."
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lines.append(f'{token_type}: "{content}"')
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else:
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lines.append(f'{token_type}')
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return '\n'.join(lines)
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def _calculate_ast_statistics(self, ast: List[Dict[str, Any]]) -> Dict[str, Any]:
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"""Calculate comprehensive AST statistics."""
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if not ast:
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return {
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'total_tokens': 0,
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'headings': {'total': 0, 'by_level': {}},
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'paragraphs': 0,
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'links': 0,
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'lists': {'ordered': 0, 'unordered': 0},
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'code_blocks': 0,
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'inline_code': 0,
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'blockquotes': 0,
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'emphasis': {'strong': 0, 'italic': 0},
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'document_structure': 'empty'
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}
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# Count token types
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token_types = Counter(token.get('type', 'unknown') for token in ast)
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# Analyze headings by level
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headings_by_level = {}
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for token in ast:
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if token.get('type') == 'heading_open':
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tag = token.get('tag', 'h1')
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level = int(tag[1:]) if tag.startswith('h') else 1
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headings_by_level[f'h{level}'] = headings_by_level.get(f'h{level}', 0) + 1
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# Count various elements
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stats = {
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'total_tokens': len(ast),
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'headings': {
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'total': token_types.get('heading_open', 0),
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'by_level': headings_by_level
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},
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'paragraphs': token_types.get('paragraph_open', 0),
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'links': token_types.get('link_open', 0),
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'lists': {
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'ordered': token_types.get('ordered_list_open', 0),
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'unordered': token_types.get('bullet_list_open', 0)
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},
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'code_blocks': token_types.get('fence', 0) + token_types.get('code_block', 0),
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'inline_code': token_types.get('code_inline', 0),
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'blockquotes': token_types.get('blockquote_open', 0),
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'emphasis': {
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'strong': token_types.get('strong_open', 0),
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'italic': token_types.get('em_open', 0)
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}
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}
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# Determine document structure
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if stats['headings']['total'] > 0:
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if stats['paragraphs'] > stats['headings']['total']:
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stats['document_structure'] = 'article'
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else:
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stats['document_structure'] = 'outline'
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elif stats['lists']['ordered'] + stats['lists']['unordered'] > 0:
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stats['document_structure'] = 'list-based'
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elif stats['paragraphs'] > 0:
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stats['document_structure'] = 'simple'
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else:
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stats['document_structure'] = 'minimal'
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return stats |